Dissertation/Thesis Abstract

Communicating Plans in Ad Hoc Multiagent Teams
by Santarra, Trevor, Ph.D., University of California, Santa Cruz, 2019, 190; 13809871
Abstract (Summary)

With the rising use of autonomous agents within robotic and software settings, agents may be required to cooperate in teams while having little or no information regarding the capabilities of their teammates. In these ad hoc settings, teams must collaborate on the fly, having no prior opportunity for coordination. Prior research in this area commonly either assumes that communication between agents is impossible given their heterogeneous design or has left communication as an open problem. Typically, to accurately predict a teammate's behavior at a future point in time, ad hoc agents leverage models learned from past experience and attempt to infer a teammate's intended strategy through observing its current course of action. However, these approaches can fail to arrive at accurate policy predictions, leaving the coordinating agent uncertain and unable to adapt to its teammates' plans. We introduce the problem of communicating minimal sets of teammate policies in order to provide information for collaboration in such ad hoc environments. We demonstrate how an agent may determine what information it should solicit from its peers but further illustrate how optimal solutions to such a problem have intractable computational requirements. Nonetheless, through the characterization of this difficulty, we identify strategies that permit approximate or heuristic approaches, allowing the practical application of this capacity in ad hoc teams.

Indexing (document details)
Advisor: Jhala, Arnav
Commitee: Smith, Adam, Spronck, Pieter
School: University of California, Santa Cruz
Department: Computer Science
School Location: United States -- California
Source: DAI-B 80/08(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Artificial intelligence, Computer science
Keywords: Active inference, Ad hoc teamwork, Teammate policies
Publication Number: 13809871
ISBN: 9781392050446
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest